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Agentic AI Design Patterns

Agentic AI design patterns are foundational blueprints and best practices for crafting intelligent systems where AI agents autonomously perceive, reason, and act within sophisticated workflows. These patterns are essential for building scalable, reliable, and dynamic AI-driven solutions that can handle open-ended challenges and real-world tasks effectively. What Are Agentic AI Design Patterns? Agentic AI design patterns encapsulate reusable architectural frameworks that define how AI agents are structured and how they operate. These patterns address organization, orchestration, autonomy, and collaboration, allowing developers to choose the optimal approach based on workload complexity, human involvement, and reliability requirements Core Design Patterns in Agentic AI Below are some of the most important agentic AI design patterns, applicable across enterprise, automation, and dynamic AI applications: Reflection Pattern: Agents evaluate and iterate on their outputs to...

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a powerful AI design pattern that combines the strengths of large language models (LLMs) with external knowledge retrieval systems. Unlike traditional language models that rely solely on pre-trained data, RAG dynamically fetches relevant information from external sources at query time, then generates responses grounded in both retrieved data and learned knowledge. This makes RAG especially effective for up-to-date, accurate, and context-aware applications Core Components of RAG Indexing (Offline Step): Data from various sources (documents, APIs, databases) is first loaded and split into manageable chunks. These chunks are then converted into vector embeddings using an embedding model and stored in a vector database optimized for fast similarity search. Retrieval (Online Step): When a user query comes in, it is transformed into a vector. The system searches the vector database to find the most relevant pieces of informatio...

In-Sprint Automation &Test Accelerators

Ever since Scrum Methodology was introduced, it has been adopted by majority of development teams there by emphasizing on in-sprint deliverables decreasing the time lag between Dev & QA. While it is important to deliver the working components at speed, it is equally important to maintain quality. This is where Test automation is crucial decreasing the time of execution and capturing defects while performing a regression check. Since agile has shorter life cycles and the agility it carries while building, it is super important to build sustainable and reliable regression suits with the same pace. The user stories must be automated by testers within the sprint covering all the layers like Unit testing, API and UI. But this can be achieved if the teams are able to build matured frameworks with in-built test accelerators that can reduce the script development life cycle across multiple browsers and platforms. This also requires a continuous collaboration between all the sta...

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